Memory-Efficient Parallel Computation of Tensor and Matrix Products for Big Tensor Decomposition

Niranjay Ravindran, Nicholas D. Sidiropoulos, Shaden Smith, and George Karypis
28th Asilomar Conference on Signals, 2014
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Low-rank tensor decomposition has many applications in signal processing and machine learning, and is becoming increasingly important for analyzing big data. A significant challenge is the computation of intermediate products which can be much larger than the final result of the computation, or even the original tensor. We propose a scheme that allows memory-efficient in-place updates of intermediate matrices. Motivated by recent advances in big tensor decomposition from multiple compressed replicas, we also consider the related problem of memory-efficient tensor compression. The resulting algorithms can be parallelized, and can exploit but do not require sparsity.
Research topics: Data mining